Principal component analysis (PCA) is a statistical technique that simplifies complex data sets by reducing the number of variables while retaining key information. PCA identifies new uncorrelated variables that capture the highest variance in the data.

Data standardization involves converting data into a standard format, which makes it easier to understand for systems and helps improve processing and data analysis.